发表机构
Southern University of Science and Technology; Faculty of Computer Science and Artificial Intelligence, Shenzhen University of Advanced Technology（南方科技大学; 深圳先进技术大学计算机科学与人工智能学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多无人机网络路径规划忽视光回传拥塞的问题，提出STWO算法，联合优化飞行距离、无线链路质量与光链路负载，实验显示峰值负载率降低56.4%，拥塞率降低72.6%。
AI 中文摘要
城市低空环境中的多无人机（UAV）网络将无人机移动性、无线接入和光回传资源耦合在一起。现有的路径规划方法优化飞行距离或无线信号质量，但仍可能将流量集中在共享的光回传链路上。我们提出了时空无线-光（STWO）规划器，一种回传感知的路径规划算法，该算法联合考虑飞行距离、无线链路质量和时变的光链路负载率。STWO在顺序多无人机规划期间更新回传占用情况，使后续无人机能够避开拥塞的光路径，同时保持无线连接。实验表明，在密集无人机部署下，STWO将峰值光链路负载率降低了高达56.4%，拥塞率降低了高达72.6%，证明了无线-光感知对于可靠的多无人机传输的重要性。
英文摘要
Multi-unmanned aerial vehicle (UAV) networks in urban low-altitude environments couple UAV mobility, wireless access, and optical backhaul resources. Existing path-planning methods optimize flight distance or wireless signal quality, but can still concentrate traffic on shared optical backhaul links. We present Spatio-Temporal Wireless-Optical (STWO) planner, a backhaul-aware path-planning algorithm that jointly considers flight distance, wireless link quality, and time-varying optical-link offered-load ratio. STWO updates backhaul occupancy during sequential multi-UAV planning, allowing later UAVs to avoid congested optical paths while maintaining wireless connectivity. Experiments show that STWO reduces peak optical-link offered-load ratio by up to 56.4\% and congestion ratio by up to 72.6\% under dense UAV deployment, demonstrating the importance of wireless-optical awareness for reliable multi-UAV transmission.
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